Download Impact of extreme weather on critical infrastructure Deliverable D2.1

This project is co-funded by the European Union under the 7th Framework Programme
Impact of extreme weather on critical infrastructure
Deliverable D2.1
Definition of different EWIs, to support the management
of European CI
D2.1 – DEFINITION OF DIFFERENT EWIS, TO SUPPORT THE MANAGEMENT OF
EUROPEAN CI
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D2.1 – DEFINITION OF DIFFERENT EWIS, TO SUPPORT THE MANAGEMENT OF
EUROPEAN CI
Project Information
Grant agreement number
606799
Project acronym
INTACT
Project full title
Impact of extreme weather on critical infrastructure
Capability Project
Start date of project
1 May 2014
Duration
36 months
Partners
TNO, CMCC, DELTARES, FAC, DRAGADOS, HRW, PANTEIA, NGI, CSIC, UNU-EHS, ULSTER, VTT
Document information
Work package
WP2: Climate and Extreme Weather
Deliverable Title
Deliverable D2.1: Definition of different EWIs, to support the management of European CI
Version
1.0
Date of submission
30 April 2015
Main Editor(s)
Edoardo Bucchignani and Jose Manuel Gutierrez
Contributor(s)
Myriam Montesarchio, Alessandra Lucia Zollo, Guido Rianna, Maialen Iturbide, Sixto Herrera,
Paola Mercogliano
Reviewer(s)
Unni Eidsvig (NGI), Peter Petiet (TNO)
This document should be
referenced as
E. Bucchignani and J.M. Gutierrez (2015), “Definition of different EWIs, to support the
management of European CI”, INTACT Deliverable D2.1, project co-funded by the European
Commission under the 7th Frame-work Programme,
Classification – This report is:
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History
Version
Issue Date
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Distribution
0.1
10 April 2015
Draft
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1.0
30 April 2015
Final
Project Officer
Security Sensitivity Assessment
According classification?
YES
If NO, please explain
Member of Security Scrutiny Board
Peter Petiet
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Contents
Contents ................................................................................................................................................. 4
Tables and figures ................................................................................................................................... 6
Glossary .................................................................................................................................................. 7
Executive summary ................................................................................................................................. 9
1
2
3
4
Introduction .................................................................................................................................. 10
1.1
The INTACT project ................................................................................................................ 10
1.2
Aim of the document ............................................................................................................. 11
1.3
Reading guide........................................................................................................................ 11
1.4
Description of methodology .................................................................................................. 12
Definitions and understanding ...................................................................................................... 13
2.1
Definition of extreme weather events ................................................................................... 13
2.2
Climate changes and Extreme events..................................................................................... 14
2.3
Impact of extreme events on infrastructures ......................................................................... 15
2.4
Changing climate and infrastructure vulnerability .................................................................. 17
Diagnosis and detection of extreme events ................................................................................... 19
3.1
Modelling of Extreme events ................................................................................................. 19
3.2
Extreme Weather Indicators (EWI) ........................................................................................ 20
3.3
Statistical modelling - Generalized Extreme Values (GEV) ...................................................... 22
3.4
Statistical modelling – Trend Analysis .................................................................................... 23
Description of conventional observational datasets adopted in the activity ................................... 24
4.1
ECA&D blended dataset......................................................................................................... 25
4.2
E-OBS .................................................................................................................................... 25
4.3
EURO4M-APGD ..................................................................................................................... 26
4.4
Spain02 ................................................................................................................................. 27
4.5
WATCH-Forcing-Data-ERA-Interim (WFDEI) ........................................................................... 28
5
Examples of application................................................................................................................. 30
6
Conclusions and future work ......................................................................................................... 36
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References .................................................................................................................................... 37
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Tables and figures
Table 3.1 Indicators based on temperature and precipitation ........................................................................................ 21
Table 3.2 Indicators based on wind, snow and humidity................................................................................................... 22
Table 4.1 Overview of observational datasets ........................................................................................................................ 24
Table 4.2 List of the WFDEI variables considered in the INTACT project .................................................................... 29
Figure 1.1 WP2 Functional architecture .................................................................................................................................... 11
Figure 2.1 Representation of Probability Density Function of temperature: effect of increase in (a) mean,
(b) variance and (c) both. ................................................................................................................................................................ 15
Figure 5.1 Examples of EWI for precipitation, wind and temperature, significant trends, evaluated
averaging over the entire Europe, for the time period 1980-2010, for the ECA&D data set. ............................. 30
Figure 5.2 Map of the trend over Europe at a local scale for some temperature EWI reported in Table 3.1
over the period 1981-2010 using the ECA&D dataset. ......................................................................................................31
Figure 5.3 Map of the trend over Europe at a local scale for some temperature EWI reported in Table 3.1
over the period 1981-2010 using the ECA&D dataset. ......................................................................................................31
Figure 5.4 Map of the trend over Europe at a local scale for some precipitation EWI reported in Table 3.1
over the period 1981-2010 using the ECA&D dataset. ......................................................................................................32
Figure 5.5 Maps of 50-years return value of precipitation, snow depth, wind speed and temperatures of
the ECA&D dataset for the period 1981-2010. ...................................................................................................................... 33
Figure 5.6 100-years return value of precipitation, snow depth, wind speed and temperatures of the
ECA&D dataset for the period 1981-2010. .............................................................................................................................. 33
Figure 5.7 Map of the trend over Europe at a local scale for some precipitation EWI reported in Table 3.1
over the period 1981-2010 using the WFDEI dataset. ........................................................................................................ 35
Figure 5.8 Map of the trend over Europe at a local scale for some precipitation EWI reported in the
Table 3.1 over the period 1981-2010 using the WFDEI dataset. ................................................................................... 35
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Glossary
CI
Critical Infrastructure
CCI
WMO’s Commission for Climatology
CEH
Centre for Ecology and Hydrology
CORDEX
Coordinated Regional Downscaling Experiment
CLIVAR
Climate Variability and Predictability
EC
European Commission
ECA&D
European Climate Assessment & Dataset
EU
European Union
ESSEM
Earth System Science and Environmental Management
ETCCDI
Experts of CCl/CLIVAR/JCOMM Team on Climate Change Detection and Indices
EUPORIAS
European Provision Of Regional Impacts Assessments on Seasonal and Decadal
Timescales (EU FP7)
EW
Extreme Weather
EWE
Extreme Weather Event
EWI
Extreme Weather Indicator
GCM
General Circulation Model
GEV
Generalized Extreme Values
FP6
Sixth Framework Programme
FP7
Seventh Framework Programme
JCOMM
Joint WMO-IOC Technical Commission for Oceanography and Marine Meteorology
IPCC
Intergovernmental Panel on Climate Change
PDF
Probability Density Function
POT
Peaks-over-threshold
RCM
Regional Climate Model
VALUE
Validating and Integrating Downscaling Methods for Climate Change Research (ESSEM
COST action)
WATCH
Water and Global Change (EU FP6)
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WCRP
World Climate Research Program
WFDEI
WATCH-Forcing-Data-ERA-Interim
WMO
World Meteorological Organization
WP
Work Package
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Executive summary
The EU FP7 project INTACT aims to support governments and managers of critical infrastructure to
reduce the risks caused by extreme weather by providing information, methods, tools and examples of
good practices. The identification of the Extreme Weather Indicators (EWIs) represents the first goal of
the Work Package (WP) 2. They will be analyzed over Europe with a special focus on the critical
infrastructures (CI) identified in the frame of WP5. It is very important to emphasize that EWIs represent
a concise way to characterize the expected changes, in frequency and intensity, of weather induced
hazards. Climate changes are the only drivers of the change considered in WP2. As reported in different
literature works, some kinds of extreme weather events might become more frequent and severe across
the globe under the effect of global warming. The evaluation of hazard changes in the next decades is
the first step to provide indications about the future risk posed by EWE and related hazard to CI. More
specifically, hazards considered by WP2 are mainly related to precipitation (incl., snowfall) winds and
temperature.
This deliverable describes the activity aimed to define appropriate EWIs for the characterization of
extreme events according to definitions and thresholds critical for infrastructures. Definitions and
guidance on climate change indicators adapted to user needs are provided, in order to meet scientific
standards with a focus on extreme events in a European climate change context.
The characterization of these extremes is performed using datasets provided from different sources or
case studies. Existing indices (e.g. ETCCDI, http://cccma.seos.uvic.ca/ETCCDI) are reviewed, while new
specific EWIs, tailored to users’ needs, are developed. Thus, beyond the typical extreme indices defined
from temperature and precipitation, multi-parameter indices are introduced, focusing on other
parameters, such as wind speed or humidity. This extension will allow to synthesize the combination
effects of meteorological variables into EWIs in order to better support the management of CI in Europe.
The specific study objectives will be defined according to the quality of the available data. To this aim
observed data (specific from case studies, or generic like the interpolated grids Iberia02, E-OBS, MAP,
etc.) and databases will be considered.
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1 Introduction
1.1 The INTACT project
Resilience of Critical Infrastructure (CI) to Extreme Weather Events (EWE), such as heavy rainfall,
drought or icing, is one of the most demanding challenges for both government and society. Extreme
Weather (EW) is a phenomenon that causes severe threats to the well-functioning of CI. The effects of
various levels of EW on CI will vary throughout Europe. These effects are witnessed through changes in
seasons and extreme temperatures (high and low), humidity (high and low), extreme or prolonged
precipitation (for example rain, fog, snow, and ice) or prolonged lack thereof (drought), extreme wind or
lack of wind, and thunderstorms. The increased frequency and intensity of EWE can cause events such
as flooding, drought, ice formation and wild fires which present a range of complex challenges to the
operational resilience of CI.
The economic and societal relevance of the dependability and resilience of CI is obvious: infrastructure
malfunctioning and outages can have far reaching consequences and impacts on economy and society.
The cost of developing and maintaining CI is high if they are expected to have a realistic functional and
economic life (50+ years). Hence, future EWE has to be taken into account when considering protection
measures, mitigation measures and adaption measures to reflect actual and predicted instances of CI
failures.
The INTACT project will address these challenges and bring together innovative and cutting edge
knowledge and experience in Europe in order to develop and demonstrate best practices in engineering,
materials, construction, planning and designing protective measures as well as crisis response and
recovery capabilities. All this will culminate in the INTACT Reference Guide, the decision support system
that facilitates cross-disciplinary and cross-border data sharing and provides for a forum for evidence
based policy formulation.
The objectives of the INTACT project are to:
•
•
Assess regionally differentiated risk throughout Europe associated with extreme weather;
Identify and classify, on a Europe wide basis, CI and to assess the resilience of such CI to the impact
of EWE;
•
Raise awareness of decision-makers and CI operators about the challenges (current and future) EW
conditions may pose to their CI; and,
•
Indicate a set of potential measures and technologies to consider and implement, be it for planning,
designing and protecting CI or for effectively preparing for crisis response and recovery.
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1.2 Aim of the document
In this section, the general objectives of WP2, and specifically of this deliverable, are defined. WP2 aims
to define appropriate EW Indicators (EWIs) characterizing the relevant critical factors for the different
infrastructures, presenting both recent trends and projections of EWIs over the 21st century. Historical
trends are presented considering observational datasets. Then performances of climate model
simulations in reproducing the different EWI are estimated and finally, future projections of the
different EWIs under different climate change scenarios are provided. Figure 1.1shows the functional
architecture of WP2.
Figure 1.1 WP2 Functional architecture
This deliverable aims:
•
•
•
•
to provide a general overview about the impact of extreme events on infrastructures,
considering a changing climate;
to define appropriate EWIs characterizing the relevant critical factors for different
infrastructures;
to provide a description of all the observational datasets used;
to provide some examples of applications that will be developed in the frame of WP2.
1.3 Reading guide
This document is organized as follows. Chapter 2 provides some basic definitions (such as the definition
of Extreme Weather Events), an overview of the impacts of Extreme Events on Infrastructures, the
problems connected with the design of infrastructures under a changing climate and the related
vulnerability. Indeed, a typical procedure to design infrastructures is to take into account extreme values
from the past historical information on climate extremes and to assume stationary mean states, but
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such measures could not be able to guarantee safety conditions in the future. Chapter 3 provides a
description of Extreme Weather Indicators which refer to moderate extremes (typically occurring
several times every year) and of Statistical Modelling, used to evaluate intensity and frequency of rare
events (that lie far in the tails of the probability distribution of variables, for example events that occur
once in 20 years). Chapter 4 provides a list and a general description of the observational datasets used
in the present work, with large emphasis on the resolution and on the data potentiality. In Chapter 5,
some examples of applications are shown and analysed, while the main Conclusions are reported in
Chapter 6.
1.4 Description of methodology
The main aim of WP2 of INTACT project is the definition of appropriate EW Indicators (EWI)
characterizing the relevant critical factors for different infrastructures, analysing present trends and
future climate projections for the 21st century.
One of the aims of the World Meteorological Organization (WMO) is to provide a regular monitoring of
the occurrence of extreme weather and climate events. In particular, the WMO’s Commission for
Climatology (CCl), the World Climate Research Program (WCRP) with Climate Variability and
Predictability (CLIVAR), and the Joint WMO-IOC Technical Commission for Oceanography and Marine
Meteorology (JCOMM) have developed tools for the analysis of computed statistics (indices). Moreover,
the Experts of CCl/CLIVAR/JCOMM Team on Climate Change Detection and Indices (ETCCDI) has
provided a core set of 27 extreme indices for temperature and precipitation, to assess changes in
extreme climate events.
In order to achieve the WP2 main aim, existing indices from ETCCDI are reviewed, while new EWI’s,
tailored to the user’s needs, are developed. In particular, in the frame of task 2.1, multiparameter
indices are defined, focusing not only on temperature and precipitation, but also on other variables, as
wind and humidity. In this way, it is possible to synthesize the combined effects of meteorological
variables into EWIs in order to better support the management of CI in Europe. More specifically,
indicators presented in Chapter 3 will be evaluated using the observational datasets described in
Chapter 4.
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2 Definitions and understanding
2.1 Definition of extreme weather events
Climate extremes and temporal deviations of weather characteristics from the norm have an important
role and strong impact on the natural environment and economic activities (Klein Tank et al, 2009). For
example, it is well known that too much or too little precipitation (e.g. flood or drought) poses severe
challenges to the society. So it is very important to detect how extremes have changed in the past, if a
change of them is expected in the future and what this change could be (e.g. changes in the intensity
and/or frequency). In this view, it is important to develop a procedure to characterize and quantify
extreme events; however, there is not a universally accepted methodology for this purpose. There is not
a unique definition of “extreme”, since it can describe either a characteristic of a climate variable or that
of an impact (Stephenson, 2008). In the case of a variable related with weather or climate (e.g.
temperature or precipitation), an extreme can be defined as a value located in the tails of the variable’s
distribution, occurring infrequently. A useful criterion is also the evaluation of the ratio of the intensity
of an anomaly in relation to climatic variability: it is generally agreed that extreme events are those
exceeding two standard deviations in long term observational series (Kislov and Krenke, 2009). In the
case of an impact, it is more difficult to define the extreme, since generally there is not a unique way to
quantify it. Moreover, a rare climate event does not necessarily causes damages; for example, a strong
wind over the Ocean generally does not result in any damage while, on the other side, floods might be
caused by not very unusual precipitation, especially in heavily urbanized basins (Peterson et al. 2012).
Weather conditions which are unfavourable but normal for a particular area (e.g. Siberian frosts or long
periods without precipitation in the desert) cannot be regarded as EWE (Kislov and Krenke, 2009).
The IPCC Assessment Report 4 (IPCC, 2007) defines an extreme climatic event as one that is rare within
its statistical reference distribution at a particular place and time. Definitions of “rare” vary, but an
extreme weather event would normally be as rare as or rarer than the 10th or 90th percentile of the
observed Probability Density Function (PDF). By definition, the characteristics of what is called extreme
weather may vary from place to place. Single extreme events cannot be simply and directly attributed to
anthropogenic climate change, as there is always a finite chance that the event in question might have
occurred naturally. When a pattern of extreme weather persists for some time, such as a season, it may
be classed as an extreme climate event, especially if it yields an average or total that is itself extreme
(e.g. drought or heavy rainfall over a season). The IPCC Special Report on managing the risks of extreme
events defines an extreme as the occurrence of a value of a variable above or below a threshold value
near the upper (or lower) ends of the range of observed values of the variable (IPCC, 2012).
Extreme events are defined not only with respect to their low frequency, but also with respect to the
intensity. For events characterized by relatively small or large values (i.e. events that have large
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magnitude deviations from the norm), one need to take into account that not all intense events are
rare. For example, low cumulative precipitations are often far from the mean precipitation but can still
occur quite frequently. Also severity is a criterion used in climate science to classify events as extreme:
events that result in large socio-economic losses. Severity is a complex criterion because damaging
impacts can occur in the absence of a rare or intense climatic event, for example thawing of mountain
permafrost leading to rock falls and mud-slides.
2.2 Climate changes and Extreme events
Design of infrastructures is generally performed under the hypothesis that climate is stationary,
meaning that physical variables could vary from day to day, but always around an unchanging mean
state. Information about weather extreme values on a specific area are generally taken from historical
series: in particular, values corresponding to a fixed return value of a variable in the historical dataset
are generally considered the normative value for design. However, this approach could be no more
adequate, since it is evident that climate changes are unequivocal and will alter the mean, variability
and extremes.
The warming of the climate system in recent decades is evident from observations and is mainly related
to the increase of anthropogenic greenhouse gas concentrations (IPCC, 2012). As a consequence, also
precipitation will be altered, since a warmer atmosphere will hold more water vapour, resulting in
heavier rains or, on the other side, in strong drought due to larger water absorption from soil and
vegetation. A changing climate may lead to changes in the frequency, intensity, spatial extent, duration
and timing of weather and climate extremes. Climate changes are usually assessed in terms of averages
climate properties rather than on variability or extremes, but often these last ones have more impacts
on the society than averages values (Katz and Brown, 1992). As climate extreme will change, it is likely
that risks for infrastructure failure will increase worldwide, since extreme weather conditions become
more variable and regionally more intense.
Figure 2.1 (a, b, c) explains how extreme events can be defined as the tails of a PDF; in particular, the
PDF of daily temperature tends to be approximately a Gaussian. More specifically, Figure 2.1(a, b, c)
shows respectively how hot and cold extremes are affected by changes in the mean, variance and in
both.
Temperature and precipitation extremes have been studied on global, regional and national scales
(Alexander et al, 2006). On the global scale, the most comprehensive analyses on temperature and
precipitation extremes are discussed in the Fourth Assessment Report of IPCC (IPCC, 2007). Some recent
changes in the pattern of extremes have been significant. Over Europe, observed trends to longer heat
waves and fewer extremely cold days have been registered. For example, since 1960, the mean heat
wave intensity over the Eastern Mediterranean area increased by a factor of five (How et al, 2013),
suggesting that the heat wave characteristics in this region have increased at higher rates than
previously reported (Kuglitsch et al, 2010). Furthermore, flood damage has increased substantially.
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However observations alone do not provide conclusive and general proof to how climate change affects
flood frequency.
Figure 2.1 Representation of Probability Density Function of temperature: effect of increase in (a) mean,
(b) variance and (c) both.
Source: http://www.garnautreview.org.au
2.3 Impact of extreme events on infrastructures
Infrastructures include transportation systems (bridges, roads, and motorways), urban buildings, energy,
water and communication systems, health-care systems and in general those sections intended to
deliver services to support the human quality of life (Wilbanks and Fernandez, 2012). Critical
Infrastructures (CI) provide fundamental functions to sustain the society (such as transnational
connecting networks) and a breakdown of a CI could lead to significant economic losses and high
number of human deaths. Moreover, a CI may rely on resources provided by other infrastructures. For
these reasons, the protection of CI from disasters is an important priority task for all countries. It is of
great importance for regional and local institutions to be aware of present and future climate extremes
related risks with regard to the development of adaptation strategies (Hokstad et al, 2012).
Climate-related extremes generally produce large impact on infrastructures, especially on those with
insufficient design. Infrastructures may become inadequate under the effects of severe extremes: for
example, the capacity of sewerages may be affected by intense rainfalls, as well as industrial
installations containing dangerous materials. Many villages in the world are dependent on wide
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infrastructure networks for power, water, transport and telecommunications, which are exposed to a
wide range of extreme events, especially because modern logistic systems are intended to minimize
redundancies (IPCC, 2012). Transport infrastructure is vulnerable to extreme value of temperature,
precipitation and wind, which can have impact on roads, rail, and airports; impacts on ports can have
serious implications on international trade, since more than 80% of international trade in goods is
carried by sea (UNCTAD, 2009). Coastal inundation may affect terminals, storage areas and intermodal
facilities especially on small islands, where transportation facilities are generally located in low elevation
coastal zones.
The design of many infrastructures, for example those related to transportation, water and energy,
require the availability of climatic data related to extreme events: for example, high precipitation might
affect the resilience of roads and bridges. The main aim, in fact, is to avoid damages to structures due to
extreme events during the whole lifetime of the infrastructures, and in the same time to limit the costs
for the realization of them. For the design of infrastructures, engineers account for climate extremes
that occur only infrequently and are not expected to recur each year. For example, design rainfalls for
sewage systems are derived estimating long period return values of maximum amount of rainfall within
1, 2, 6, 12 and 24 hours (Zhang and Zwiers, 2013), making use of powerful statistical tools based on
extreme value theory, to aid the analysis of historical series (Coles, 2001): such tools have been
developed to infer extreme values that might occur beyond the range of the observed sample, such as
the estimation of the 100-years return value on the basis of a 50 year series of historical values.
Impact of extreme events is being investigated in the frame of EUPORIAS (EUPORIAS, 2014) and VALUE
(VALUE, 2013) projects. EUPORIAS (European Provision Of Regional Impacts Assessments on Seasonal
and Decadal Timescales) has been funded by the European commission under the 7th framework
program, with the aim of developing and delivering reliable predictions of the impacts of future climatic
conditions on a number of key sectors (water, energy, health, transport, agriculture and tourism), on
timescales from seasons to years. VALUE (Validating and Integrating Downscaling Methods for Climate
Change Research) is a COST Action whose aim is to provide a European network to validate and develop
downscaling methods and improve the collaboration between the research communities and with
stakeholders. The Action systematically compares the different downscaling approaches and assesses
temporal variability from sub-daily to decadal time scales including climate change, extreme events,
spatial coherence and variability, and inter-variable consistency together with the related uncertainties.
In particular, a WP is devoted to carry out an inventory of extreme definition, a validation phase and
development of downscaling methods for spatially extended extremes.
The characterization of EW is performed according with thresholds critical for infrastructures. Absolute
thresholds are suitable in order to monitor extreme events that affect human society and the natural
environment, while percentile thresholds are specific of the sites, since they are expressions of
anomalies relative to the local climate. Moreover, specific threshold values related to stakeholder’s and
user’s needs are considered.
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2.4 Changing climate and infrastructure vulnerability
The severity of climate impacts on infrastructures will vary across Europe according to specific locations
and their geophysical risk exposure, the existing adaptive capacity and resilience, and the level of
economic development. Evaluation of potential effects of climate change on infrastructure is still very
limited and further research and development will be required to support decision-making. Experiences
over the past periods have shown how vulnerable infrastructures can be to the types of EWE that are
projected to be more intense and more frequent with future climate change. Evaluation of vulnerability
of infrastructures requires the analysis of several climatic elements and their impact on the resilience.
The world’s largest reinsurance company, Munich Re, calculated that more than 90 percent of all
disasters and 65 percent of associated economic damages were weather and climate related (Munich
Re, 2011). Insurance generally report an increase in the number of weather-related events, which have
caused significant losses, for example, wind-storms and floods in Europe. However, there is still
insufficient information about the extent to which these changes can be found in wind and precipitation
observations and whether they are driven by global warming. Some of the hazard-driven increases of
events may have been hidden by human prevention actions, in particular in the case of flood, as these
can be influenced much more by preventive measures than wind-storm losses.
Analysis of vulnerability can be done from a global or local view, for example by assessing the
vulnerability of a city, a river basin or a specific piece of infrastructure. Vulnerabilities and impacts are
issues beyond physical infrastructures themselves: the true consequences of impacts involve not only
the costs associated with the replacement of affected infrastructures but also social and environmental
effects, since supply chains are interrupted, economic activities are suspended, social well-being is
threatened (Wilbanks and Fernandez, 2012).
Vulnerability alarms tend to be focused on EWE associated with climate change that can interfere with
infrastructure services, often cascading across different infrastructures due to wide interdependencies,
especially where populations and activities are concentrated in urban areas. Vulnerabilities are larger
when infrastructures are subject to multiple stresses, when they are located in areas vulnerable to EWE
and if climate change is severe. These risks are greater for infrastructures that are located near
particularly climate-sensitive environmental features, such as coastlines, rivers, storm tracks and
vegetation in arid areas. A larger risk is expected for infrastructures already stressed by age or by
demand levels that exceed what they were designed to supply (Wilbanks and Fernandez, 2012).
The consequences of climate change will be different, depending on the kinds of infrastructures:
•
The consequences for transport infrastructure such as rail, roads, shipping and aviation will differ
from region to region. In particular, the projected increase in the frequency and intensity of EWE
such as heavy rain, snowfall, extreme heat and cold, drought and reduced visibility can increase
negative impacts on transport infrastructures, causing damages and economic losses, transport
disruptions and delays (European Commission, 2013).
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•
•
Climate changes will have effects on energy transmission, distribution, generation and demand. In
fact, the generation of electrical energy is affected by efficiency reduction due to climate change
(e.g. decreasing availability of cooling water for electricity generators). However, in some parts of
Europe, increased precipitation or more wind may also lead to better opportunities for hydropower
or wind energy generation. Furthermore, extreme weather periods, such as heat waves or cold
spells, will cause higher energy demand peaks, causing overstress of energy infrastructure
(European Commission, 2013).
Buildings and infrastructures can be vulnerable because of their design (e.g. low resistance to
storms) or location (e.g. in flood-prone areas, landslides, avalanches). Many European cities have
been built along a river, and these rivers will respond to extreme rainfall or snowmelt events with
extreme discharges, threatening the cities with floods (European Commission, 2013).
Implications of climate change for infrastructures can be examined by assessing historical data with
extreme weather events and by simulating future conditions, including both individual events and either
a series of extreme events in a short time period or the combination of an extreme weather event with
another type of threat at the same time (Wilbanks and Kates, 2010).
As explained in Sec. 3.1, Regional Climate Models are used to provide high resolution climate projections
on the area of interest over the 21st century, with different emission scenarios. According with WMO
average values
recommendations (http://www.wmo.int/pages/themes/climate/climate_data_and_products.php),
over 30-years periods are used, as they are long enough to filter out any interannual variation or
anomalies, but also short enough to be able to show longer climatic trends. Generally the current
climate period is calculated over 1961-1990 (or 1971-2000) time period, while the following time
horizons are selected for future projections:
1) 2011-2040 (short range)
2) 2041-2070 (medium range)
3) 2071-2100 (long range)
These time periods are selected to provide insight into changes in climatic parameters over this century
and to be representative of future time frames for planning infrastructure design or rehabilitation
cycles. Future vulnerability assessments are related to time frames that match the design or remaining
service life of existing infrastructures of the service life for new infrastructure. Generally, data necessary
for the vulnerability assessments are available and scenarios of climate change are possible for almost
all climate indicators. However, the quality or usefulness of the data (both observed and predicted)
varies greatly, as well as their level of confidence. Indeed, the data from the models must be validated
by the scientific community. As research continues and additional validation work is done, more
products become available, although this depends largely on the needs expressed by end-users and the
importance given to produce this information. One of the main problems linked to data availability
arises from a lack of observed data, namely for events that are very localized in time and space which,
by nature, are rare.
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3 Diagnosis and detection of extreme events
3.1 Modelling of Extreme events
Assessment of trends and changes in weather extremes is complicated and harder to predict because
they are rare events, related to sharp changes in climatic system and result from its nonlinear nature
(Kislov and Krenke, 2009). The main tool for providing insights into possible future climate changes is the
climate modelling. Climate models are mathematical models that simulate the climate system’s
behaviour based on the fundamental laws of physics.
General Circulation Models (GCMs) simulate planet-wide climate dynamics: they are powerful
instruments to simulate the response of the global climate system to external forcing (Giorgi, 2005),
however they are generally unsuitable to simulate local climate, since they are characterized by
resolutions generally around or coarser than 100 km, which is too poor for impact studies, since many
important phenomena occur at spatial scales of few tens of km. Moreover, GCMs do not account for
vegetation variations, complex topography and coastlines, which are important aspects of the physical
response governing the regional climate change signal.
One of the most effective tools, providing high resolution climate analysis through dynamical
downscaling, is represented by Regional Climate Models (RCM) (Giorgi and Mearns, 1991), able to
provide an accurate description of climate variability on local scale. Moreover, RCMs show the capability
to provide a detailed description of climate extremes (Rummukainen, 2010; Soares et al, 2012).
A very relevant research question would be the determination of changes of extreme events expected
under anthropogenic climate change in future climate model simulations. The capabilities of RCMs were
assessed over Europe in the framework of several European projects, such as PRUDENCE (Christensen
and Christensen, 2007) and ENSEMBLES (Van Der Linden and Mitchell, 2009). In recent years, the WCRP
Coordinated Regional Downscaling Experiment (CORDEX) project (Giorgi et al, 2009) has been
established to provide a global coordination of regional climate downscaling for improved climate
change adaptation policy and impact assessment: EURO-CORDEX is the European branch of the CORDEX
initiative. To date there is a lack of an exhaustive validation of EURO-CORDEX data in terms of extreme
values. Preliminary analysis of climate projections collected in EURO-CORDEX project show that in the
future a significant increase in the frequency of events, such as heavy rainfall, heat waves and droughts,
is expected (Jacob et al, 2014; Vautard et al, 2013). Confidence in projections of future changes in the
severity and frequency of such events will increase if the mechanisms of changes can be identified and
understood. Equally important is the rigorous quantification of the uncertainties in these projections,
including the natural variability of the climate system, the limitations in climate models and the
statistical methods used to analyse their output (Wehner, 2013).
Several works highlight evidences that RCM skill in simulating the spatial and temporal characteristics of
rainfall increases with increasing model resolution (Maraun et al., 2010); moreover, a higher grid spacing
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is expected to improve parameters such as meso-scale circulations and precipitation intensity
distribution at daily scale (Kotlarski et al, 2014). The main inconvenience of regional climate models is
that they are computationally demanding, this feature gives some constraints on the resolution, domain
size, number of experiments and duration of simulations that can be conducted.
Overall, a general agreement in the research community is emerged about the likely future pattern of
extreme weather events in Europe: heat waves will become more frequent while the number of cold
spells and frost days are likely to decrease. Southern Europe and the Mediterranean Region will be
affected by a combination of a reduction in annual precipitation and an increase in average
temperatures (Giorgi and Lionello, 2008). High intensity and extreme precipitation are expected to
become more frequent over the 21st century. The increased frequency is estimated to be larger for more
extreme events, but will vary from region to region (Beninston et al, 2007).
Numerical tools are being used in the evaluation of extreme events simulated in regional climate
models, in the characterization of the influence of large scale atmospheric circulation variations on
extreme precipitation and in the detection of anthropogenic influence on temperature extremes (Zwiers
et al, 2011)
3.2 Extreme Weather Indicators (EWI)
The ETCCDI core set of 27 extreme indices for temperature and precipitation
(http://etccdi.pacificclimate.org/index.shtml ) has been extended to other variables (wind, snow,
humidity, etc.) within the ECA&D project (http://eca.knmi.nl/indicesextremes/index.php). These indices
highlight various characteristics of extremes, including frequency, amplitude and persistence (Klein Tank
et al, 2009) and are widely used to assess future changes (e.g. Fischer et al. 2013). Some indices involve
calculation of the number of days in a year/season exceeding specific thresholds. On the one hand,
percentile thresholds are specific of the sites, since they are expressions of anomalies relative to the
local climate. For example, the number of days with daily minimum temperature below the 10th
percentile value in the 1961-1990 base period depends on the season (e.g. a minimum temperature of 10°C could be considered not an extreme in winter, but would be very extreme in other seasons) and is
also related to the geographical location. On the other hand, absolute thresholds are suitable in order to
monitor extreme events that affect human society and the natural environment. Examples of such
indices are the following: maximum 24 hours precipitation amount, widely used in engineering
applications to infer design values for engineered structures, or the number of frost days per year (i.e.
minimum temperature below 0°C). Such indices may not be applicable everywhere on the Earth, since
the phenomena may not occur in some places (e.g. a frost day would never occur in the tropics),
however they have a long history in many applications.
To build the final EWI dictionary to be used in the framework of the INTACT project, we have considered
several sources. Although, most of the indices are included in the extended list defined within the
ECA&D project, we have also considered some indicators used by the Weather Meteorological Services
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to establish weather alerts and warnings, and other combined indicators. The definition and details of
the indices considered are included in Table 3.1and Table 3.2:
Table 3.1 Indicators based on temperature and precipitation
Temperature
FD
SU
ID
TR
GSL
TXx
TNx
TXn
TNn
TN10p
TX10p
TN90p
TX90p
WSDI
CSDI
DTR
FTD
HW
Precipitation
Rx1day
Rx5day
SDII
RR1
R10mm
R20mm
RNNmm
CDD
CWD
R95pTOT
R99pTOT
PRCPTOT
RAI
RDI
Combined
DW
DC
WW
WC
FRD
Description
Number of frost (TN<0ºC) days
Number of summer (TX>25ºC) days
Number of icing (TX<0ºC) days
Number of tropical (TN>20ºC) nights
Growing season length
Maximum value of daily maximum temperature
Maximum value of daily minimum temperature
Minimum value of daily maximum temperature
Minimum value of daily minimum temperature
Number of days when TN < 10th percentile
Number of days when TX < 10th percentile
Number of days when TN > 90th percentile
Number of days when TX > 90th percentile
Warm-spell duration index
Cold-spell duration index
Daily temperature range
Number of days with temperature zero-crossings (Frost-thaw cycles)
Number of heat waves (TX>35ºC) days
Description
Maximum 1-day precipitation
Maximum consecutive 5-day precipitation
Simple precipitation intensity index
Annual count of days when PRCP≥ 1mm
Annual count of days when PRCP≥ 10mm
Annual count of days when PRCP≥ 20mm
Annual count of days when PRCP≥ NNmm
Maximum length of dry spell (consecutive days with RR < 1mm)
Maximum length of wet spell (consecutive days with RR ≥ 1mm)
Annual total PRCP when RR > 95p
Annual total PRCP when RR > 99p
Annual total precipitation in wet days
Number of days when TN > 90th percentile
Number of days when TX > 90th percentile
Description
Number of dry (RR<0.1 mm)-warm (TG>75th percentile) days
Number of dry (RR<0.1 mm)-cold (TG<25th percentile) days
Number of wet (RR<0.1 mm)-warm (TG>75th percentile) days
Number of wet (RR<0.1 mm)-cold (TG<25th percentile) days
Number of freezing rain (TX<0ºC y RR>0.5 mm)
Units
Days
Days
Days
Days
Days
ºC
ºC
ºC
ºC
days
days
days
days
days
days
ºC
days
days
Units
mm
mm
mm
days
days
days
days
days
days
mm
mm
mm
1
mm
Units
days
days
days
days
days
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These indicators allow the analysis of the interannual variability and trend of the mean and extreme
weather regimes, both in the historical period and the future projections given by the models. Note that
the models have important bias, most of them non-systematic, and, then, the effect of these biases
should be taken into account in the calculation of the indices, mainly in the case of absolute thresholddependent indicators. In this sense, percentile threshold are more robust than the absolute ones.
Table 3.2 Indicators based on wind, snow and humidity.
Wind
FG
FGx1day
FGXx1day
FG05
FG10
FG15
FG25
FGNN
Snow
SD1
SD010
SD1020
SDx1day
SDratio
Combined
HU90
Description
Monthly average of daily mean wind speed
Yearly maximum of daily mean wind speed
Yearly maximum of daily maximum wind speed of gusts
Number of days with wind speed > 5 m/s
Number of days with wind speed > 10 m/s
Number of days with wind speed > 15 m/s
Number of days with wind speed > 25 m/s
Number of days with wind speed > NN m/s
Description
Number of days with snow cover
Number of days with snow depth 0-10 cm
Number of days with snow depth 10-20 cm
Yearly maximum snowfall
Average total annual / seasonal snowfall
Description
Number of days when the relative humidity (daily mean) is above 90%
and mean temperature > 10 ºC
Units
m/s
m/s
m/s
days
days
Days
Days
Days
Units
Days
Days
Days
Mm
1
Units
Days
3.3 Statistical modelling - Generalized Extreme Values (GEV)
The descriptive indices developed by ETCCDI refer to moderate extremes that typically occur several
times every year. On the other side, but complementary, intensity and frequency of rare events, in
terms of return periods and values, are evaluated using the extreme value theory. This approach allows
estimating the intensity of events which occur once in a given period, typically 50, 100 or 1000 years. To
this aim, two general approaches are possible:
•
•
Peaks-over- threshold method (POT), which adjusts the occurrence and intensity of events
above a predefined threshold.
Explicit extreme value theory based on Generalized Extreme Value (GEV), which is, by the
extreme value theorem, the limit distribution of properly normalized maxima of a sequence of
independent and identically distributed random variables.
Once fitted, the model provides a cumulative distribution function, F(x), which can be used to estimate
return values for different time spans (T), usually expressed in years, which are typically used to design,
maintain and adapt the infrastructures. Changes in these return values lead to changes in the intensity
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of extreme events. For example, where extremes increase, the impact will be a reduction in the
“effective” return period event that existing structures were built to withstand.
In this case, return periods and values are estimated for a climatological period, typically 30 years, and,
then, the possible changes found are referred to this time-scale. To this aim, for each location and
variable the parameters of the GEV distribution (location, scale and shape) were estimated using the
method of maximum log-likelihood and, then, the return values for 50 and 100 years were obtained.
Equivalent analyses using the POT approach leads to similar results and, then, for the sake of the
simplicity, has not been included in the present document.
3.4 Statistical modelling – Trend Analysis
In order to analysis the historical evolution of the EWI described in Table 3.1and Table 3.2, linear
regression models (least squared method) were fitted against time to obtain the values of linear trend
and to calculate the change between the end and beginning of the studied period. Furthermore, the
Mann-Kendall non-parametric test was used for detecting the statistical significance of trend (Kendall,
1975). A significance P-level <0.05 (95% of confidence) was set to reject the null hypothesis of the test,
that is, no trend in data. This confidence threshold was used in all the figures shown in the present
document.
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4 Description of conventional observational datasets
adopted in the activity
The characterization of extreme weather events is performed using datasets provided from different
sources or case studies. This specific study objective will be defined according to the quality of the
available data.
This Chapter provides a list and a general description of the observational datasets used in the present
work, with large emphasis on the resolution and on the data potentiality. Using these observational
datasets we can evaluate the indicators presented in Chapter 3. Each dataset mentioned in Table 4.1
will be described in the next sections.
Table 4.1 Overview of observational datasets
Dataset
Spatial characteristic
Spatial Extent
Resolution
Temporal characteristics
Period
Resolution
Parameters
Maximum, minimum and mean
temperature,
precipitation
amount, mean sea level
pressure, cloud cover, humidity,
snow depth, sunshine duration,
mean wind speed, maximum
wind gust, wind direction.
Maximum, minimum and mean
temperature, precipitation and
mean sea level pressure
ECA&D
Europe and
Mediterranean
Point
stations
different
periods for
different
stations
Daily
E-OBS
Europe and
Mediterranean
0.25º (~25
km)
1950-2013
Daily
5x5 km
1971-2008
Daily
Precipitation (rainfall plus snow
water equivalent)
0.11º,
0.22º and
0.44º
1971-2007
Daily
Maximum, minimum and mean
temperature, precipitation
3-hourly, 6hourly and
daily
Temperature, wind speed,
surface
pressure,
specific
humidity, long- and short-wave
downwards surface radiation,
rainfall and snowfall rate.
EURO4MAPGD
Spain02
WFDEI
24
European Alps
and adjacent
flatland
Peninsular Spain
and the Balearic
islands
Worldwide –
Land areas
0.5ºx0.5º
1979-2012
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4.1 ECA&D blended dataset
The European Climate Assessment & Dataset (ECA&D, http://eca.knmi.nl/) project was initiated by the
ECSN (http://www.eumetnet.eu/ecsn) in 1998 to provide of high quality climate data, products and
services to the European user community. This project is receiving actually data from 66 participants for
62 countries, containing 40630 series of observations for 12 elements at 10259 meteorological stations
throughout Europe and the Mediterranean, most of them publicly available for non-commercial
research and education (http://eca.knmi.nl/documents/ECAD_datapolicy.pdf).
Although both blended and non-blended ECA series are available (Klein Tank et al, 2002), only blended
series are further analysed in ECA&D and used for gridding and, then, this is the data set considered
within the INTACT project. Blended series are series that are near-complete by infilling from nearby
stations and using synoptical messages (more details in http://eca.knmi.nl/documents/atbd.pdf).
Meteorological observations are taken at many stations across Europe, each day. To minimize the
effects of changes over time in the way the measurements were made, rigorous quality control is
applied before the data is used to analyse extremes. The blended series have been tested for
homogeneity, which is relevant to assess the quality of each series for climate change research.
The meteorological parameters included in this data set are daily values of maximum, minimum and
mean temperature, precipitation amount, mean sea level pressure, cloud cover, humidity, snow depth,
sunshine duration, wind speed and direction, and maximum wind gust. Note that the observational
network and spatial coverage depend strongly on the variable considered.
On the one hand, the availability of several meteorological parameters at a local scale and for the
European and Mediterranean region is the main advantage of this dataset because it allows analyse in
deep the trend of all this variables, and the indices derived from them, in the historical period.
On the other hand, this dataset has been used to build E-OBS (Haylock et al. 2008, van den Besselaar et
al. 2011), also considered for INTACT, and, thus, the analysis of both dataset lets study the impact/effect
of the gridding process in the extreme events at a local scale.
4.2 E-OBS
The E-OBS dataset (Haylock et al., 2008) is a European daily high-resolution (0.25°) gridded dataset for
precipitation, mean, maximum and minimum temperature for the period 1950-2012, developed in the
frame of EU ENSEMBLES project (Van Der Linden and Mitchell, 2009) with the aim of using it for
validation of Regional Climate Models and for climate change studies. It was constructed through
interpolation of ECA&D station data (the most complete collection of station data over Europe); the
number of stations used for the interpolation differs in time and by variable, in particular the period
1961–1990 has the highest density, with more precipitation than temperature stations. Additional
station data were obtained from other research projects, such as STARDEX (Haylock et al., 2006), or by
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requesting at various National Meteorological Services. The map of the station network shows uneven
station distribution, with the highest station density in the UK, Netherlands and Switzerland.
The E-OBS dataset was obtained applying a three stage process: monthly mean values of temperature
and precipitation were first interpolated to a rotated pole 0.1° grid using three dimensional thin plate
splines; daily anomalies (departure from the monthly mean) were interpolated on the same grid and
combined with the monthly mean grid (interpolation was performed applying the kriging method);
Finally, the 0.1° grid values were used to compute area-average values at the E-OBS grid resolution.
The clear advantage of E-OBS is its spatial and temporal coverage: it represents a valuable standard
reference dataset for climate research and is widely used for climate model evaluation over Europe.
However, this dataset is affected by a number of potential inaccuracies: typical errors include incorrect
station location and inhomogeneities in the station time series. Moreover, interpolation accuracy
decreases as the network density decreases (e.g. in southern Europe, especially in Italy, few stations are
available) and degrades in complex terrains, such as mountain areas. Hofstra et al. (2009) assessed EOBS with respect to homogeneity of gridded data, finding, according to the Wijngaard test (Wijngaard et
al., 2003), many suspect areas for both temperature and precipitation, especially related to the period
1980-1990. Inhomogeneities may lead to meaningless results for trend analysis. Hofstra et al. (2009)
also compared E-OBS to existing datasets developed with denser station networks, finding excellent
correlations but large mean absolute errors. In particular, with respect to the ELDAS dataset (Rubel et
al., 2004), there is a tendency to underestimate precipitation over some areas; a negative bias was also
found with respect to the MAP dataset (Frei and Schar, 1998). E-OBS precipitation data were assessed
over the Greater Alpine Region (GAR) in Turco et al. (2013): their results suggest that E-OBS does not
provide reliable climatology over north western Italy and should be treated with caution, especially for
extreme indices over GAR.
4.3 EURO4M-APGD
The EURO4M-APGD dataset (Isotta et al., 2013) represents an enhancement of the trans-Alpine
precipitation dataset MAP of Frei and Schar (1998). It was developed in the frame of EU project
EURO4M (European Reanalysis and Observations for Monitoring), whose aim was the preparation and
analysis of datasets for monitoring European climate variations from in situ and satellite observations
and from model-based regional reanalyses (International Innovation, 2011).
It is a daily gridded dataset for precipitation with spacing of 5 km constructed with a distance-angular
weighting scheme that integrates climatological precipitation–topography relationships. The analyses
are based on high-resolution rain-gauge data from seven Alpine countries (Austria, Croatia, France,
Germany, Italy, Slovenia and Switzerland), with 5500 measurements per day on average, spanning the
period 1971–2008. For Austria, France, Germany and Switzerland, the renewed dataset carries on the
high data density of the MAP dataset, essentially by extending previously available station records in
time, with a density of one station per 80-150 km2.
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For Italy, the major contribution was provided by the archive ARCIS (Archivio Climatologico per l’Italia
Settentrionale), which represents a coordination effort of the regional services In northern Italy
promoting the exchange and common analysis of long term climate data (Pavan et al., 2013). For
Slovenia and Croatia, new datasets have been integrated in the Alpine rain-gauge dataset, providing a
valuable contribution for the south-eastern part of the domain. The data collection effort resulted in a
significant improvement of data density in previously under-sampled regions and led to more
homogenous data coverage.
All institutions contributing to the dataset have applied their native quality control procedures before
providing data. However, to remedy frequent problems of data quality as evident during the
climatological analyses, the consistency and quality of the different data contributions has been
ensured; this has been achieved using a three steps quality checking procedure: the scanning of time
series for coding problems, a fully automatic spatial consistency check, and the identification of overall
suspicious time series.
Spatial interpolation procedure does not substantially differ from that for the earlier datasets; changes
involve new settings of the method’s parameters that allow better reproduction of fine-scale variations
in regions with dense station coverage. A climatological precipitation–topography relationship was
included in order to improve the reliability of the resulting grid dataset. The adopted method of spatial
analysis relies on the widely used anomaly concept where separate analyses are calculated for some
reference condition, typically a long-term mean, and for the relative anomaly from that reference on the
day under consideration (Widmann and Bretherton, 2000). Multiplication of the anomaly and reference
grids finally yields the daily precipitation analysis.
Data are available over the domain from 2 - 17.5° E to 43 – 49° N. The domain extends over about 1200
km from Central France to eastern Austria and over about 700 km from northern Italy to southern
Germany. Slovenia and a part of Croatia are also included in the domain. The high resolution will not
resolve precipitation at the scale of the grid spacing, but it will improve estimation of spatial averages
over complex domain shapes. This is particularly useful in hydrological applications requiring mean
precipitation over catchments, or for the evaluation of regional climate models, when the observational
grid dataset needs to be assembled onto the model’s native grid structure.
4.4 Spain02
Spain02 (http://www.meteo.unican.es/en/datasets/spain02) is a series of high-resolution daily
precipitation and (maximum, minimum and mean) temperature gridded datasets developed for
peninsular Spain and the Balearic islands (Herrera et al. 2011, 2012, 2015). A dense network of
approximately 2500 quality-controlled stations (250 for temperatures) was selected from the Spanish
Meteorological Agency (AEMET) in order to build the gridded products in three different resolutions
(0.11º, 0.22º and 0.44º in rotated coordinates matching Euro-CORDEX grids) and four interpolation
approaches (OK, AA-OK, AA-2D and AA-3D, see Herrera et al. 2015 for more details) for the period 19712007.
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In the framework of the INTACT project the three resolutions are considered but only the AA-3D
interpolation method, due to its better representation of the temperature, its comparability with the EOBS data set, also considered within the project, and because it provides areal representative values,
needed to properly evaluate the regional climate model outputs.
Different studies have shown the capability of this data set to reproduce both mean and extreme
regimes in a region with a great climatic variability like the Iberian Peninsula. The latest version of
Spain02 is one of the reference data sets used in different initiatives like the Action Cost VALUE
(http://www.value-cost.eu/) or CORDEX (http://wcrp-cordex.ipsl.jussieu.fr/) to calibrate and validate
the output of global and regional climate models.
In comparison with other data sets included within the INTACT project, the main shortcoming of Spain02
is the variables available which do not include all the parameters needed to build the EWIs considered in
this project.
4.5 WATCH-Forcing-Data-ERA-Interim (WFDEI)
The Integrated Project Water and Global Change (WATCH, http://www.eu-watch.org/), funded under
the EU FP6, brought together the hydrological, water resources and climate communities to analyse,
quantify and predict the components of the current and future global water cycles and related water
resources states, and to evaluate their uncertainties and clarify the overall vulnerability of global water
resources related to the main societal and economic sectors.
The WATCH project has produced a large number of data sets publicly available, via the Centre for
Ecology and Hydrology (CEH) Gateway catalogue (https://gateway.ceh.ac.uk/), which are of
considerable use in regional and global studies of climate and water (Weedon et al. 2011 and Haddeland
et al 2011).
In the framework of the INTACT Project, the WATCH-Forcing-Data-ERA-Interim (WFDEI, http://www.euwatch.org/gfx_content/documents/README-WFDEI(1).pdf) data set will be considered, which has been
build applying the WFD methodology (Weedon et al. 2011) to ERA-Interim data (Weedon et al. 2014).
This data set includes 3-hourly (Average over previous 3 hours) and daily (averages of the 3-hourly data
for the current day) meteorological variables (seeTable 4.2) for the global land surface, including
Antarctica, at 0.5ºx0.5º resolution for the period 1979-2012. In order to complete the list of variables
needed within the INTACT project, the three latest variables included in Table 4.2 have been derived
from the 3-hourly data.
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Table 4.2 List of the WFDEI variables considered in the INTACT project
Variable
Tas
wss
Ps
huss
rlds
rsds
Pr
prsn
tasmax
tasmin
wssmax
Description
2 m air temperature (instantaneous)
10 m wind speed (instantaneous)
Surface pressure (instantaneous)
2 m specific humidity (instantaneous)
Long-wave downwards surface radiation flux
(average over previous 3 hours)
Long-wave downwards surface radiation flux
(average over previous 3 hours)
Rainfall rate (average over previous 3 hours)
Snowfall rate (average over previous 3 hours)
Derived Variables
2 m daily maximum air temperature.
2 m daily minimum air temperature.
10 m daily maximum wind speed
Units
K
m/s
Pa
kg/kg
Time
3h / day
3h / day
3h / day
3h / day
W/m2
3h / day
W/m2
3h / day
kg/m2s
kg/m2s
3h / day
3h / day
K
K
Day
Day
day
m/s
The main shortcoming of this data set is its medium-low spatial resolution (0.5ºx0.5º). However, the
high temporal resolution (3-hourly and daily) of WDFEI and the variables available allow the analysis of a
great diversity of events, covering the high climatic variability of Europe.
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5 Examples of application
Over the last decades, several regions of the world have experienced extreme events with enormous
consequences on society and ecosystems. To evaluate the impact of the climatic change in the
frequency and intensity of these events in the future, it is necessary to analyse the present conditions
and trends of them with different approaches and with different observational datasets.
At a European scale, the large climate variability of this region makes difficult to evaluate this change in
the extreme events and only some of the indices defined in Table 3.1 and Table 3.2 show a significant
trend in the last 30 years, as shown in Figure 5.1.
Figure 5.1 Examples of EWI for precipitation, wind and temperature, significant trends, evaluated averaging
over the entire Europe, for the time period 1980-2010, for the ECA&D data set.
NORTH, WEST and EAST indicate the numbers of days in which the wind has the corresponding direction. The
number in the title is the trend value. Only the indices with a significant trend have been included.
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D2.1 – DEFINITION OF DIFFERENT EWIS, TO SUPPORT THE MANAGEMENT OF
EUROPEAN CI
In Figure 5.1, an increasing of the intensity and frequency of heavy rainy days is shown. For the wind
speed and direction, a decreasing of the intensity and the frequency of strong winds is reflected but also
the calm days decrease and a change in the more frequent directions have been found. Finally, the
changes in temperatures affect mainly to the minimum temperatures, leading to changes in the daily
range.
Figure 5.2 Map of the trend over Europe at a local scale for some temperature EWI reported in Table 3.1 over
the period 1981-2010 using the ECA&D dataset.
FD: frost days; ID: ice days; FTD: frost-thaw cycles; SU: summer days; TR: tropical nights; HW: heat waves.
If we analyse more in details the trends in Europe at a local scale, there are many differences between
regions and indices. For example, Figure 5.2 and Figure 5.3 show the significant trends for several
temperature indicators. In the case of the summer days (SU) there is a global increasing trend in all
Europe but, in the case of tropical nights (TR) or heat waves (HW) we can find a dipole with a stable
situation in the north and a positive trend in the south. Other most noisy and complex spatial patterns
could be found.
Figure 5.3 Map of the trend over Europe at a local scale for some temperature EWI reported in Table 3.1 over
the period 1981-2010 using the ECA&D dataset.
TXN: minimum of maximum temperature; TXX: maximum of maximum temperature; GSL: growing season
th
th
length; TN90P: days with TN>90 percentile; TX90P: days with TX>90 percentile.
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D2.1 – DEFINITION OF DIFFERENT EWIS, TO SUPPORT THE MANAGEMENT OF
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In the case of precipitation (Figure 5.4), with the exception of the frequency of dry days which tends to
decrease in almost all Europe, there is not a common trend in the region, as could be expected.
Figure 5.4 Map of the trend over Europe at a local scale for some precipitation EWI reported in Table 3.1 over
the period 1981-2010 using the ECA&D dataset.
RR1: days with PRCP≥1mm; CWD: consecutive wet days; RX1DAY: maximum 1-day precipitation; PRCPTOT:
annual precipitation; CDD: consecutive dry days; RX5DAY: maximum 5-day precipitation.
Considering the return values for 50 and 100 years of the different variables we can only analyse or
estimate what are the value which can be expected to occur once in a given period. Changes in these
return values are associated with the increase/decrease of the occurrence of extreme events in a
climatological period. In Figure 5.5 and Figure 5.6 the return values for 50 and 100 years are shown.
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D2.1 – DEFINITION OF DIFFERENT EWIS, TO SUPPORT THE MANAGEMENT OF
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Some of the European climatic characteristics are reflected in these figures, for example the temperate
climate or the extreme character of the precipitation in the Mediterranean.
Figure 5.5 Maps of 50-years return value of precipitation, snow depth, wind speed and temperatures of the
ECA&D dataset for the period 1981-2010.
Figure 5.6 100-years return value of precipitation, snow depth, wind speed and temperatures of the ECA&D
dataset for the period 1981-2010.
In order to analyse the uncertainty and robustness of these results, is advisable to compare them with
those obtained with other data sets. In this case, we repeated the same analysis with the WFDEI data
set introduced in the previous section, considering the available variables of this data set (
33
D2.1 – DEFINITION OF DIFFERENT EWIS, TO SUPPORT THE MANAGEMENT OF
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Figure 5.7 and Figure 5.8).
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D2.1 – DEFINITION OF DIFFERENT EWIS, TO SUPPORT THE MANAGEMENT OF
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Figure 5.7 Map of the trend over Europe at a local scale for some precipitation EWI reported in Table 3.1 over
the period 1981-2010 using the WFDEI dataset.
In the case of precipitation, the regions with significant trends are scarce and very sparse, and no global
conclusions can be obtained.
Figure 5.8 Map of the trend over Europe at a local scale for some precipitation EWI reported in the Table 3.1
over the period 1981-2010 using the WFDEI dataset.
For temperature there is more agreement between the data sets than for precipitation, including the
dipole between the absence of trend in the north and the increasing trend in the south.
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D2.1 – DEFINITION OF DIFFERENT EWIS, TO SUPPORT THE MANAGEMENT OF
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6 Conclusions and future work
This document aimed to describe the activity finalized to define appropriate EWIs for the
characterization of extreme events according to definitions and thresholds critical for infrastructures, in
a European climate change context. There is not a universally accepted procedure for this purpose, since
it is not possible to provide a unique definition of “extreme”. In this work, we have referred to the IPCC
Assessment Report 4 (IPCC, 2007), which defines an extreme climatic event as one that is rare within its
statistical reference distribution at a particular place and time. To gain a uniform perspective on
observed changes in in climate extremes, ETCCDI has defined a core set of descriptive indices of
extremes, which has been extended to other variables within the ECA&D project. In this work, we have
also considered some indicators used by Weather Meteorological Services to establish weather alerts
and warnings, and other combined indicators. This extension will allow synthesizing the combination
effects of meteorological variables into EWIs in order to better support the management of CI in Europe.
Design is generally performed under the hypothesis that climate is stationary; however, this approach
could be no more adequate, since it is evident that climate changes are unequivocal and will alter the
mean, variability and extremes. Examples of EWIs significant trends have been shown, obtained
averaging observational data over the entire European area or over some subdomains. They highlight
that, at European scale, the large climate variability makes difficult to evaluate this change in the
extreme events and only some of the indices show a significant trend over the last thirty years.
In the following of the activity, evaluation of EWIs related historical data series provided by high
resolution RCM simulations will be performed. Since, as already shown, results strongly depend on the
quality of the data considered; there would be an uncertainty source to be considered in the evaluation
of the RCMs. In the same way, future climate projections from RCMs will be analysed for different
European zones and different time horizons, considering periods of at least thirty years. It is well known
that, to obtain regional or local projections, high resolution data sets are needed with all the target
variables used to define the EWIs: limitations related to the spatial resolution of available simulations
(about 10 km) must be taken into account, as well as limitations connected with time resolution, since
data at daily resolution are currently available at most.
The characterization of EW will be performed according with thresholds critical for infrastructures.
Typical threshold values will be generally taken from literature works. Absolute thresholds are suitable
in order to monitor extreme events that affect human society and the natural environment, while
percentile thresholds are specific of the sites, since they are expressions of anomalies relative to the
local climate. Moreover, specific threshold values related to stakeholder’s and user’s needs will be
considered. Since model outputs are affected by biases, most of them non-systematic, it is necessary to
take into account the effects of them in the calculation of the indices, mainly in the case of absolute
threshold-dependent indicators. In this sense, percentile thresholds are more robust than the absolute
ones.
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